Overview

Dataset statistics

Number of variables50
Number of observations988
Missing cells40434
Missing cells (%)81.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory386.1 KiB
Average record size in memory400.1 B

Variable types

Categorical28
Numeric21
Unsupported1

Alerts

Date has a high cardinality: 276 distinct values High cardinality
Canada_export has a high cardinality: 154 distinct values High cardinality
Usa_export has a high cardinality: 154 distinct values High cardinality
India_export has a high cardinality: 142 distinct values High cardinality
Russia_export has a high cardinality: 108 distinct values High cardinality
South_Africa_export has a high cardinality: 154 distinct values High cardinality
Turkey has a high cardinality: 272 distinct values High cardinality
Brazil has a high cardinality: 275 distinct values High cardinality
France_export has a high cardinality: 240 distinct values High cardinality
Germeny_export has a high cardinality: 178 distinct values High cardinality
United Kingdome_export has a high cardinality: 153 distinct values High cardinality
China_export has a high cardinality: 108 distinct values High cardinality
Australia _import has a high cardinality: 109 distinct values High cardinality
Canada_import has a high cardinality: 154 distinct values High cardinality
Usa_import has a high cardinality: 154 distinct values High cardinality
India_import has a high cardinality: 142 distinct values High cardinality
Russia_import has a high cardinality: 108 distinct values High cardinality
South_Africa_import has a high cardinality: 154 distinct values High cardinality
Turkey_import has a high cardinality: 168 distinct values High cardinality
Brazil_import has a high cardinality: 168 distinct values High cardinality
France_import has a high cardinality: 240 distinct values High cardinality
Germeny_import has a high cardinality: 178 distinct values High cardinality
United Kingdome_import has a high cardinality: 154 distinct values High cardinality
China_import has a high cardinality: 108 distinct values High cardinality
Japan_import has a high cardinality: 153 distinct values High cardinality
Domestic Market (Contract) Blow Molding, Low is highly correlated with Spot/Export Blow Molding and 14 other fieldsHigh correlation
Spot/Export Blow Molding is highly correlated with Domestic Market (Contract) Blow Molding, Low and 11 other fieldsHigh correlation
Spot, Domestic is highly correlated with Domestic Market (Contract) Blow Molding, Low and 12 other fieldsHigh correlation
WTISPLC is highly correlated with Domestic Market (Contract) Blow Molding, Low and 11 other fieldsHigh correlation
MCOILBRENTEU is highly correlated with Domestic Market (Contract) Blow Molding, Low and 13 other fieldsHigh correlation
GASREGM is highly correlated with Domestic Market (Contract) Blow Molding, Low and 15 other fieldsHigh correlation
IMPCH is highly correlated with Domestic Market (Contract) Blow Molding, Low and 10 other fieldsHigh correlation
EXPCH is highly correlated with Domestic Market (Contract) Blow Molding, Low and 15 other fieldsHigh correlation
PRUBBUSDM is highly correlated with Domestic Market (Contract) Blow Molding, Low and 8 other fieldsHigh correlation
WPUFD4111 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 13 other fieldsHigh correlation
PCU325211325211 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 18 other fieldsHigh correlation
PCU32611332611301 is highly correlated with Spot, Domestic and 12 other fieldsHigh correlation
WPU0915021625 is highly correlated with EXPCH and 10 other fieldsHigh correlation
PCU3252132521 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 16 other fieldsHigh correlation
MHHNGSP is highly correlated with Spot/Export Blow Molding and 3 other fieldsHigh correlation
WPU072205011 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 17 other fieldsHigh correlation
PCU32611132611115 is highly correlated with GASREGM and 10 other fieldsHigh correlation
PCU32611332611301.1 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 18 other fieldsHigh correlation
PCU32611132611112 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 16 other fieldsHigh correlation
WPU0915021622 is highly correlated with IMPCH and 11 other fieldsHigh correlation
Producer Price Index by Industry: Plastics Material and Resins Manufacturing: Thermoplastic Resins and Plastics Materials is highly correlated with Domestic Market (Contract) Blow Molding, Low and 18 other fieldsHigh correlation
Domestic Market (Contract) Blow Molding, Low is highly correlated with Spot/Export Blow Molding and 14 other fieldsHigh correlation
Spot/Export Blow Molding is highly correlated with Domestic Market (Contract) Blow Molding, Low and 9 other fieldsHigh correlation
Spot, Domestic is highly correlated with Domestic Market (Contract) Blow Molding, Low and 13 other fieldsHigh correlation
WTISPLC is highly correlated with Domestic Market (Contract) Blow Molding, Low and 11 other fieldsHigh correlation
MCOILBRENTEU is highly correlated with Domestic Market (Contract) Blow Molding, Low and 12 other fieldsHigh correlation
GASREGM is highly correlated with Domestic Market (Contract) Blow Molding, Low and 15 other fieldsHigh correlation
IMPCH is highly correlated with Domestic Market (Contract) Blow Molding, Low and 10 other fieldsHigh correlation
EXPCH is highly correlated with Domestic Market (Contract) Blow Molding, Low and 14 other fieldsHigh correlation
PRUBBUSDM is highly correlated with Domestic Market (Contract) Blow Molding, Low and 4 other fieldsHigh correlation
WPUFD4111 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 13 other fieldsHigh correlation
PCU325211325211 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 16 other fieldsHigh correlation
PCU32611332611301 is highly correlated with WPU0915021625 and 5 other fieldsHigh correlation
WPU0915021625 is highly correlated with Spot, Domestic and 12 other fieldsHigh correlation
PCU3252132521 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 15 other fieldsHigh correlation
MHHNGSP is highly correlated with Spot, Domestic and 5 other fieldsHigh correlation
WPU072205011 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 17 other fieldsHigh correlation
PCU32611132611115 is highly correlated with Spot, Domestic and 13 other fieldsHigh correlation
PCU32611332611301.1 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 18 other fieldsHigh correlation
PCU32611132611112 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 14 other fieldsHigh correlation
WPU0915021622 is highly correlated with Spot, Domestic and 13 other fieldsHigh correlation
Producer Price Index by Industry: Plastics Material and Resins Manufacturing: Thermoplastic Resins and Plastics Materials is highly correlated with Domestic Market (Contract) Blow Molding, Low and 16 other fieldsHigh correlation
Domestic Market (Contract) Blow Molding, Low is highly correlated with Spot/Export Blow Molding and 11 other fieldsHigh correlation
Spot/Export Blow Molding is highly correlated with Domestic Market (Contract) Blow Molding, Low and 5 other fieldsHigh correlation
Spot, Domestic is highly correlated with Domestic Market (Contract) Blow Molding, Low and 8 other fieldsHigh correlation
WTISPLC is highly correlated with Domestic Market (Contract) Blow Molding, Low and 9 other fieldsHigh correlation
MCOILBRENTEU is highly correlated with Domestic Market (Contract) Blow Molding, Low and 8 other fieldsHigh correlation
GASREGM is highly correlated with Domestic Market (Contract) Blow Molding, Low and 8 other fieldsHigh correlation
IMPCH is highly correlated with EXPCH and 5 other fieldsHigh correlation
EXPCH is highly correlated with Domestic Market (Contract) Blow Molding, Low and 6 other fieldsHigh correlation
PRUBBUSDM is highly correlated with Domestic Market (Contract) Blow Molding, Low and 4 other fieldsHigh correlation
WPUFD4111 is highly correlated with IMPCH and 8 other fieldsHigh correlation
PCU325211325211 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 14 other fieldsHigh correlation
PCU32611332611301 is highly correlated with WPU0915021625 and 5 other fieldsHigh correlation
WPU0915021625 is highly correlated with WPUFD4111 and 8 other fieldsHigh correlation
PCU3252132521 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 10 other fieldsHigh correlation
MHHNGSP is highly correlated with Spot, Domestic and 1 other fieldsHigh correlation
WPU072205011 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 14 other fieldsHigh correlation
PCU32611132611115 is highly correlated with WPUFD4111 and 9 other fieldsHigh correlation
PCU32611332611301.1 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 18 other fieldsHigh correlation
PCU32611132611112 is highly correlated with PCU325211325211 and 8 other fieldsHigh correlation
WPU0915021622 is highly correlated with WPUFD4111 and 6 other fieldsHigh correlation
Producer Price Index by Industry: Plastics Material and Resins Manufacturing: Thermoplastic Resins and Plastics Materials is highly correlated with Domestic Market (Contract) Blow Molding, Low and 14 other fieldsHigh correlation
Domestic Market (Contract) Blow Molding, Low is highly correlated with Spot/Export Blow Molding and 21 other fieldsHigh correlation
Spot/Export Blow Molding is highly correlated with Domestic Market (Contract) Blow Molding, Low and 19 other fieldsHigh correlation
Spot, Domestic is highly correlated with Domestic Market (Contract) Blow Molding, Low and 20 other fieldsHigh correlation
WTISPLC is highly correlated with Domestic Market (Contract) Blow Molding, Low and 22 other fieldsHigh correlation
MCOILBRENTEU is highly correlated with Domestic Market (Contract) Blow Molding, Low and 21 other fieldsHigh correlation
GASREGM is highly correlated with Domestic Market (Contract) Blow Molding, Low and 21 other fieldsHigh correlation
IMPCH is highly correlated with Domestic Market (Contract) Blow Molding, Low and 17 other fieldsHigh correlation
EXPCH is highly correlated with Domestic Market (Contract) Blow Molding, Low and 20 other fieldsHigh correlation
PRUBBUSDM is highly correlated with Domestic Market (Contract) Blow Molding, Low and 14 other fieldsHigh correlation
WPUFD4111 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 22 other fieldsHigh correlation
PCU325211325211 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 22 other fieldsHigh correlation
PCU32611332611301 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 20 other fieldsHigh correlation
WPU0915021625 is highly correlated with Spot/Export Blow Molding and 18 other fieldsHigh correlation
PCU3252132521 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 21 other fieldsHigh correlation
MHHNGSP is highly correlated with Domestic Market (Contract) Blow Molding, Low and 19 other fieldsHigh correlation
WPU072205011 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 18 other fieldsHigh correlation
PCU32611132611115 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 20 other fieldsHigh correlation
PCU32611332611301.1 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 21 other fieldsHigh correlation
PCU32611132611112 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 19 other fieldsHigh correlation
WPU0915021622 is highly correlated with Domestic Market (Contract) Blow Molding, Low and 20 other fieldsHigh correlation
Producer Price Index by Industry: Plastics Material and Resins Manufacturing: Thermoplastic Resins and Plastics Materials is highly correlated with Domestic Market (Contract) Blow Molding, Low and 22 other fieldsHigh correlation
Australia _export is highly correlated with Domestic Market (Contract) Blow Molding, Low and 13 other fieldsHigh correlation
Saudi_export is highly correlated with Domestic Market (Contract) Blow Molding, Low and 21 other fieldsHigh correlation
Saudi_import is highly correlated with Domestic Market (Contract) Blow Molding, Low and 21 other fieldsHigh correlation
Date has 712 (72.1%) missing values Missing
Domestic Market (Contract) Blow Molding, Low has 712 (72.1%) missing values Missing
Spot/Export Blow Molding has 803 (81.3%) missing values Missing
Spot, Domestic has 863 (87.3%) missing values Missing
WTISPLC has 712 (72.1%) missing values Missing
MCOILBRENTEU has 712 (72.1%) missing values Missing
GASREGM has 712 (72.1%) missing values Missing
IMPCH has 713 (72.2%) missing values Missing
EXPCH has 713 (72.2%) missing values Missing
PRUBBUSDM has 713 (72.2%) missing values Missing
WPUFD4111 has 713 (72.2%) missing values Missing
PCU325211325211 has 713 (72.2%) missing values Missing
PCU32611332611301 has 713 (72.2%) missing values Missing
WPU0915021625 has 856 (86.6%) missing values Missing
PCU3252132521 has 760 (76.9%) missing values Missing
MHHNGSP has 712 (72.1%) missing values Missing
WPU072205011 has 873 (88.4%) missing values Missing
PCU32611132611115 has 873 (88.4%) missing values Missing
PCU32611332611301.1 has 784 (79.4%) missing values Missing
PCU32611132611112 has 873 (88.4%) missing values Missing
WPU0915021622 has 855 (86.5%) missing values Missing
Producer Price Index by Industry: Plastics Material and Resins Manufacturing: Thermoplastic Resins and Plastics Materials has 712 (72.1%) missing values Missing
Australia _export has 950 (96.2%) missing values Missing
Canada_export has 834 (84.4%) missing values Missing
Saudi_export has 955 (96.7%) missing values Missing
Usa_export has 834 (84.4%) missing values Missing
India_export has 846 (85.6%) missing values Missing
Russia_export has 880 (89.1%) missing values Missing
South_Africa_export has 834 (84.4%) missing values Missing
Turkey has 716 (72.5%) missing values Missing
Brazil has 713 (72.2%) missing values Missing
France_export has 748 (75.7%) missing values Missing
Germeny_export has 810 (82.0%) missing values Missing
United Kingdome_export has 835 (84.5%) missing values Missing
China_export has 880 (89.1%) missing values Missing
Australia _import has 879 (89.0%) missing values Missing
Canada_import has 834 (84.4%) missing values Missing
Saudi_import has 955 (96.7%) missing values Missing
Usa_import has 834 (84.4%) missing values Missing
India_import has 846 (85.6%) missing values Missing
Russia_import has 880 (89.1%) missing values Missing
South_Africa_import has 834 (84.4%) missing values Missing
Turkey_import has 820 (83.0%) missing values Missing
Brazil_import has 820 (83.0%) missing values Missing
France_import has 748 (75.7%) missing values Missing
Germeny_import has 810 (82.0%) missing values Missing
United Kingdome_import has 834 (84.4%) missing values Missing
China_import has 880 (89.1%) missing values Missing
Japan_import has 835 (84.5%) missing values Missing
South_korea_import has 988 (100.0%) missing values Missing
Date is uniformly distributed Uniform
Australia _export is uniformly distributed Uniform
Canada_export is uniformly distributed Uniform
Saudi_export is uniformly distributed Uniform
Usa_export is uniformly distributed Uniform
India_export is uniformly distributed Uniform
Russia_export is uniformly distributed Uniform
South_Africa_export is uniformly distributed Uniform
Turkey is uniformly distributed Uniform
Brazil is uniformly distributed Uniform
France_export is uniformly distributed Uniform
Germeny_export is uniformly distributed Uniform
United Kingdome_export is uniformly distributed Uniform
China_export is uniformly distributed Uniform
Australia _import is uniformly distributed Uniform
Canada_import is uniformly distributed Uniform
Saudi_import is uniformly distributed Uniform
Usa_import is uniformly distributed Uniform
India_import is uniformly distributed Uniform
Russia_import is uniformly distributed Uniform
South_Africa_import is uniformly distributed Uniform
Turkey_import is uniformly distributed Uniform
Brazil_import is uniformly distributed Uniform
France_import is uniformly distributed Uniform
Germeny_import is uniformly distributed Uniform
United Kingdome_import is uniformly distributed Uniform
China_import is uniformly distributed Uniform
Japan_import is uniformly distributed Uniform
South_korea_import is an unsupported type, check if it needs cleaning or further analysis Unsupported
PCU32611332611301 has 72 (7.3%) zeros Zeros

Reproduction

Analysis started2023-06-23 08:42:44.495020
Analysis finished2023-06-23 08:44:22.718496
Duration1 minute and 38.22 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct276
Distinct (%)100.0%
Missing712
Missing (%)72.1%
Memory size7.8 KiB
01-04-2005
 
1
01-12-2012
 
1
01-07-2013
 
1
01-12-2022
 
1
01-01-2004
 
1
Other values (271)
271 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)100.0%

Sample

1st row01-01-2000
2nd row01-02-2000
3rd row01-03-2000
4th row01-04-2000
5th row01-05-2000

Common Values

ValueCountFrequency (%)
01-04-20051
 
0.1%
01-12-20121
 
0.1%
01-07-20131
 
0.1%
01-12-20221
 
0.1%
01-01-20041
 
0.1%
01-10-20181
 
0.1%
01-05-20121
 
0.1%
01-06-20071
 
0.1%
01-06-20131
 
0.1%
01-03-20221
 
0.1%
Other values (266)266
 
26.9%
(Missing)712
72.1%

Length

2023-06-23T14:14:23.077193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01-04-20051
 
0.4%
01-08-20131
 
0.4%
01-06-20001
 
0.4%
01-09-20181
 
0.4%
01-10-20061
 
0.4%
01-08-20101
 
0.4%
01-12-20001
 
0.4%
01-03-20141
 
0.4%
01-05-20191
 
0.4%
01-04-20011
 
0.4%
Other values (266)266
96.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Domestic Market (Contract) Blow Molding, Low
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct66
Distinct (%)23.9%
Missing712
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean72.37681159
Minimum32
Maximum108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:23.233431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile41
Q157.75
median73
Q386
95-th percentile103
Maximum108
Range76
Interquartile range (IQR)28.25

Descriptive statistics

Standard deviation19.41309882
Coefficient of variation (CV)0.2682226309
Kurtosis-0.9302487727
Mean72.37681159
Median Absolute Deviation (MAD)14
Skewness-0.191896076
Sum19976
Variance376.8684058
MonotonicityNot monotonic
2023-06-23T14:14:23.420938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6715
 
1.5%
7215
 
1.5%
8312
 
1.2%
9310
 
1.0%
479
 
0.9%
859
 
0.9%
808
 
0.8%
1008
 
0.8%
1048
 
0.8%
767
 
0.7%
Other values (56)175
 
17.7%
(Missing)712
72.1%
ValueCountFrequency (%)
321
 
0.1%
331
 
0.1%
341
 
0.1%
352
 
0.2%
361
 
0.1%
373
0.3%
381
 
0.1%
402
 
0.2%
416
0.6%
426
0.6%
ValueCountFrequency (%)
1083
 
0.3%
1072
 
0.2%
1048
0.8%
1034
 
0.4%
1008
0.8%
982
 
0.2%
973
 
0.3%
964
 
0.4%
955
0.5%
9310
1.0%

Spot/Export Blow Molding
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct88
Distinct (%)47.6%
Missing803
Missing (%)81.3%
Infinite0
Infinite (%)0.0%
Mean57.97972973
Minimum28
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:23.670903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile35
Q149
median59
Q367
95-th percentile78.4
Maximum92
Range64
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.11619508
Coefficient of variation (CV)0.2262203556
Kurtosis-0.2029274296
Mean57.97972973
Median Absolute Deviation (MAD)9
Skewness-0.01278474933
Sum10726.25
Variance172.0345733
MonotonicityNot monotonic
2023-06-23T14:14:23.905276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
507
 
0.7%
707
 
0.7%
646
 
0.6%
596
 
0.6%
556
 
0.6%
565
 
0.5%
735
 
0.5%
475
 
0.5%
695
 
0.5%
444
 
0.4%
Other values (78)129
 
13.1%
(Missing)803
81.3%
ValueCountFrequency (%)
281
 
0.1%
291
 
0.1%
301
 
0.1%
311
 
0.1%
323
0.3%
342
0.2%
353
0.3%
371
 
0.1%
381
 
0.1%
391
 
0.1%
ValueCountFrequency (%)
921
 
0.1%
901
 
0.1%
873
0.3%
852
0.2%
801
 
0.1%
791
 
0.1%
78.51
 
0.1%
781
 
0.1%
772
0.2%
761
 
0.1%

Spot, Domestic
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct74
Distinct (%)59.2%
Missing863
Missing (%)87.3%
Infinite0
Infinite (%)0.0%
Mean60.396
Minimum33.25
Maximum100.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:24.092768image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum33.25
5-th percentile40.85
Q153
median59
Q367.5
95-th percentile86
Maximum100.75
Range67.5
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation12.64748413
Coefficient of variation (CV)0.2094093008
Kurtosis1.367213695
Mean60.396
Median Absolute Deviation (MAD)6
Skewness0.8040641366
Sum7549.5
Variance159.9588548
MonotonicityNot monotonic
2023-06-23T14:14:24.264632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
618
 
0.8%
576
 
0.6%
536
 
0.6%
624
 
0.4%
53.54
 
0.4%
71.53
 
0.3%
63.53
 
0.3%
583
 
0.3%
543
 
0.3%
523
 
0.3%
Other values (64)82
 
8.3%
(Missing)863
87.3%
ValueCountFrequency (%)
33.251
0.1%
35.51
0.1%
37.751
0.1%
39.252
0.2%
401
0.1%
40.751
0.1%
41.251
0.1%
41.751
0.1%
431
0.1%
43.251
0.1%
ValueCountFrequency (%)
100.751
0.1%
98.751
0.1%
971
0.1%
92.51
0.1%
91.751
0.1%
87.51
0.1%
86.251
0.1%
851
0.1%
84.51
0.1%
76.251
0.1%

WTISPLC
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct272
Distinct (%)98.6%
Missing712
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean62.50568841
Minimum16.55
Maximum133.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:24.460351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum16.55
5-th percentile27
Q141
median59.28
Q384.17
95-th percentile104.8475
Maximum133.93
Range117.38
Interquartile range (IQR)43.17

Descriptive statistics

Standard deviation26.06592234
Coefficient of variation (CV)0.4170168029
Kurtosis-0.7743644115
Mean62.50568841
Median Absolute Deviation (MAD)20
Skewness0.3308918756
Sum17251.57
Variance679.4323075
MonotonicityNot monotonic
2023-06-23T14:14:24.647841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.672
 
0.2%
45.182
 
0.2%
94.622
 
0.2%
26.272
 
0.2%
31.681
 
0.1%
112.571
 
0.1%
65.571
 
0.1%
93.211
 
0.1%
49.781
 
0.1%
40.751
 
0.1%
Other values (262)262
 
26.5%
(Missing)712
72.1%
ValueCountFrequency (%)
16.551
0.1%
19.331
0.1%
19.672
0.2%
20.741
0.1%
22.211
0.1%
24.421
0.1%
25.521
0.1%
25.741
0.1%
25.881
0.1%
26.272
0.2%
ValueCountFrequency (%)
133.931
0.1%
133.441
0.1%
125.391
0.1%
116.611
0.1%
114.841
0.1%
112.571
0.1%
110.041
0.1%
109.551
0.1%
108.51
0.1%
106.571
0.1%

MCOILBRENTEU
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct274
Distinct (%)99.3%
Missing712
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean65.40376812
Minimum18.38
Maximum132.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:24.991566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum18.38
5-th percentile25.47
Q142.7275
median62.77
Q385.5875
95-th percentile113.4775
Maximum132.72
Range114.34
Interquartile range (IQR)42.86

Descriptive statistics

Standard deviation29.61218255
Coefficient of variation (CV)0.4527595795
Kurtosis-0.9322815845
Mean65.40376812
Median Absolute Deviation (MAD)21.525
Skewness0.3423278185
Sum18051.44
Variance876.8813552
MonotonicityNot monotonic
2023-06-23T14:14:25.179056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.622
 
0.2%
25.662
 
0.2%
109.541
 
0.1%
33.631
 
0.1%
25.681
 
0.1%
24.081
 
0.1%
49.781
 
0.1%
65.321
 
0.1%
56.861
 
0.1%
57.811
 
0.1%
Other values (264)264
 
26.7%
(Missing)712
72.1%
ValueCountFrequency (%)
18.381
0.1%
18.711
0.1%
18.81
0.1%
19.421
0.1%
20.281
0.1%
20.541
0.1%
22.761
0.1%
23.71
0.1%
24.081
0.1%
24.341
0.1%
ValueCountFrequency (%)
132.721
0.1%
132.321
0.1%
125.451
0.1%
123.261
0.1%
122.81
0.1%
122.711
0.1%
119.751
0.1%
119.331
0.1%
117.251
0.1%
116.971
0.1%

GASREGM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct258
Distinct (%)93.5%
Missing712
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean2.570398551
Minimum1.086
Maximum4.929
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:25.382164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.086
5-th percentile1.39925
Q11.982
median2.555
Q33.149
95-th percentile3.78425
Maximum4.929
Range3.843
Interquartile range (IQR)1.167

Descriptive statistics

Standard deviation0.7807818742
Coefficient of variation (CV)0.3037590704
Kurtosis-0.6126027034
Mean2.570398551
Median Absolute Deviation (MAD)0.5835
Skewness0.201322064
Sum709.43
Variance0.6096203351
MonotonicityNot monotonic
2023-06-23T14:14:25.569671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5553
 
0.3%
2.8363
 
0.3%
2.8592
 
0.2%
1.872
 
0.2%
3.6112
 
0.2%
2.292
 
0.2%
2.3662
 
0.2%
2.8032
 
0.2%
1.3972
 
0.2%
1.8412
 
0.2%
Other values (248)254
 
25.7%
(Missing)712
72.1%
ValueCountFrequency (%)
1.0861
0.1%
1.1071
0.1%
1.1141
0.1%
1.1711
0.1%
1.2491
0.1%
1.2891
0.1%
1.3151
0.1%
1.3771
0.1%
1.3821
0.1%
1.3861
0.1%
ValueCountFrequency (%)
4.9291
0.1%
4.5591
0.1%
4.4441
0.1%
4.2221
0.1%
4.1091
0.1%
4.0621
0.1%
4.0541
0.1%
3.9751
0.1%
3.9061
0.1%
3.91
0.1%

IMPCH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct275
Distinct (%)100.0%
Missing713
Missing (%)72.2%
Infinite0
Infinite (%)0.0%
Mean29591.89422
Minimum6375.6
Maximum52081.0705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:25.772782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum6375.6
5-th percentile8410.24
Q120508.60567
median31563.98816
Q339575.40592
95-th percentile47118.88148
Maximum52081.0705
Range45705.4705
Interquartile range (IQR)19066.80026

Descriptive statistics

Standard deviation12303.5267
Coefficient of variation (CV)0.4157735431
Kurtosis-1.02318286
Mean29591.89422
Median Absolute Deviation (MAD)8918.29815
Skewness-0.3419864465
Sum8137770.909
Variance151376769.4
MonotonicityNot monotonic
2023-06-23T14:14:25.960265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45724.951241
 
0.1%
43798.098421
 
0.1%
22581.562741
 
0.1%
45335.351721
 
0.1%
23038.880361
 
0.1%
24382.852731
 
0.1%
29052.438351
 
0.1%
16887.762591
 
0.1%
25656.561281
 
0.1%
40319.618351
 
0.1%
Other values (265)265
 
26.8%
(Missing)713
72.2%
ValueCountFrequency (%)
6375.61
0.1%
6424.11
0.1%
6584.41
0.1%
6902.11
0.1%
7070.51
0.1%
72591
0.1%
7409.11
0.1%
7590.21
0.1%
7604.71
0.1%
7686.81
0.1%
ValueCountFrequency (%)
52081.07051
0.1%
50348.836581
0.1%
49938.04991
0.1%
49525.453031
0.1%
49247.89151
0.1%
48625.30611
0.1%
48360.676271
0.1%
48133.149631
0.1%
48104.793421
0.1%
47986.674411
0.1%

EXPCH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct275
Distinct (%)100.0%
Missing713
Missing (%)72.2%
Infinite0
Infinite (%)0.0%
Mean7176.49547
Minimum863.1
Maximum16678.51002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:26.132136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum863.1
5-th percentile1514.6
Q13644.347996
median8080.515063
Q39741.468033
95-th percentile12970.85272
Maximum16678.51002
Range15815.41002
Interquartile range (IQR)6097.120037

Descriptive statistics

Standard deviation3764.254058
Coefficient of variation (CV)0.5245253862
Kurtosis-0.9468193766
Mean7176.49547
Median Absolute Deviation (MAD)2822.86842
Skewness0.01361576314
Sum1973536.254
Variance14169608.61
MonotonicityNot monotonic
2023-06-23T14:14:26.319600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13027.082591
 
0.1%
8383.3101241
 
0.1%
2034.4282271
 
0.1%
3807.4915721
 
0.1%
8960.1549771
 
0.1%
1398.91
 
0.1%
6733.7185481
 
0.1%
8478.1173081
 
0.1%
12988.65731
 
0.1%
6201.2963781
 
0.1%
Other values (265)265
 
26.8%
(Missing)713
72.2%
ValueCountFrequency (%)
863.11
0.1%
972.71
0.1%
1187.51
0.1%
1227.51
0.1%
1289.81
0.1%
1330.51
0.1%
1333.31
0.1%
1335.61
0.1%
1398.91
0.1%
1427.81
0.1%
ValueCountFrequency (%)
16678.510021
0.1%
15865.223361
0.1%
15698.339651
0.1%
15576.0061
0.1%
14802.584481
0.1%
14508.082551
0.1%
14206.533471
0.1%
13629.874341
0.1%
13375.086891
0.1%
13343.61191
0.1%

PRUBBUSDM
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct275
Distinct (%)100.0%
Missing713
Missing (%)72.2%
Infinite0
Infinite (%)0.0%
Mean89.57946296
Minimum22.11990723
Maximum280.7876187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:26.507089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum22.11990723
5-th percentile28.27212314
Q160.83112909
median78.40857432
Q3107.5938581
95-th percentile178.4791195
Maximum280.7876187
Range258.6677115
Interquartile range (IQR)46.76272901

Descriptive statistics

Standard deviation46.39098729
Coefficient of variation (CV)0.5178752558
Kurtosis2.302074739
Mean89.57946296
Median Absolute Deviation (MAD)21.96865659
Skewness1.296227161
Sum24634.35231
Variance2152.123702
MonotonicityNot monotonic
2023-06-23T14:14:26.694578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.862737841
 
0.1%
76.804619391
 
0.1%
152.94504921
 
0.1%
77.175593591
 
0.1%
128.71008071
 
0.1%
56.439917731
 
0.1%
82.575509181
 
0.1%
30.571000951
 
0.1%
100.30343211
 
0.1%
150.41553231
 
0.1%
Other values (265)265
 
26.8%
(Missing)713
72.2%
ValueCountFrequency (%)
22.119907231
0.1%
23.347364781
0.1%
24.369938661
0.1%
25.727438631
0.1%
26.018785151
0.1%
26.253498851
0.1%
26.292151021
0.1%
26.603690461
0.1%
27.300406931
0.1%
27.484121481
0.1%
ValueCountFrequency (%)
280.78761871
0.1%
265.49246581
0.1%
250.36168631
0.1%
245.78227621
0.1%
232.07400821
0.1%
223.80274151
0.1%
215.27519481
0.1%
214.64016471
0.1%
212.11300421
0.1%
206.45281271
0.1%

WPUFD4111
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct225
Distinct (%)81.8%
Missing713
Missing (%)72.2%
Infinite0
Infinite (%)0.0%
Mean185.9782655
Minimum135
Maximum278.025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:26.866440image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile139.05
Q1155.65
median193.4
Q3208.6
95-th percentile236.4304
Maximum278.025
Range143.025
Interquartile range (IQR)52.95

Descriptive statistics

Standard deviation31.71268368
Coefficient of variation (CV)0.1705182248
Kurtosis-0.5730106592
Mean185.9782655
Median Absolute Deviation (MAD)19.6
Skewness0.1429143505
Sum51144.023
Variance1005.694306
MonotonicityNot monotonic
2023-06-23T14:14:27.038303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142.33
 
0.3%
2083
 
0.3%
207.63
 
0.3%
156.33
 
0.3%
208.33
 
0.3%
166.33
 
0.3%
212.33
 
0.3%
139.23
 
0.3%
205.93
 
0.3%
203.72
 
0.2%
Other values (215)246
 
24.9%
(Missing)713
72.2%
ValueCountFrequency (%)
1351
0.1%
1362
0.2%
137.21
0.1%
137.31
0.1%
137.41
0.1%
137.51
0.1%
137.61
0.1%
137.91
0.1%
1381
0.1%
138.22
0.2%
ValueCountFrequency (%)
278.0251
0.1%
268.3121
0.1%
265.6561
0.1%
263.3151
0.1%
262.9811
0.1%
259.2271
0.1%
258.9281
0.1%
255.7981
0.1%
250.9171
0.1%
245.6181
0.1%

PCU325211325211
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct255
Distinct (%)92.7%
Missing713
Missing (%)72.2%
Infinite0
Infinite (%)0.0%
Mean249.2298873
Minimum141.2
Maximum380.764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:27.225791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum141.2
5-th percentile154.86
Q1222.7
median258.6
Q3279.15
95-th percentile357.6337
Maximum380.764
Range239.564
Interquartile range (IQR)56.45

Descriptive statistics

Standard deviation55.30843454
Coefficient of variation (CV)0.2219173436
Kurtosis-0.06873029035
Mean249.2298873
Median Absolute Deviation (MAD)26.8
Skewness-0.03374783887
Sum68538.219
Variance3059.022931
MonotonicityNot monotonic
2023-06-23T14:14:27.397659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
273.83
 
0.3%
287.12
 
0.2%
2402
 
0.2%
273.22
 
0.2%
261.22
 
0.2%
167.62
 
0.2%
233.52
 
0.2%
276.92
 
0.2%
2542
 
0.2%
233.42
 
0.2%
Other values (245)254
 
25.7%
(Missing)713
72.2%
ValueCountFrequency (%)
141.21
0.1%
141.71
0.1%
142.71
0.1%
1431
0.1%
144.11
0.1%
146.21
0.1%
146.31
0.1%
149.91
0.1%
150.61
0.1%
151.51
0.1%
ValueCountFrequency (%)
380.7641
0.1%
379.4491
0.1%
377.2111
0.1%
377.0611
0.1%
376.961
0.1%
375.3981
0.1%
373.7171
0.1%
371.4021
0.1%
370.661
0.1%
367.8831
0.1%

PCU32611332611301
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct164
Distinct (%)59.6%
Missing713
Missing (%)72.2%
Infinite0
Infinite (%)0.0%
Mean148.1542945
Minimum0
Maximum312.788
Zeros72
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:27.710138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median181.4
Q3224.45
95-th percentile284.7933
Maximum312.788
Range312.788
Interquartile range (IQR)224.45

Descriptive statistics

Standard deviation98.39990725
Coefficient of variation (CV)0.6641718187
Kurtosis-1.161205405
Mean148.1542945
Median Absolute Deviation (MAD)51.5
Skewness-0.4935387333
Sum40742.431
Variance9682.541747
MonotonicityNot monotonic
2023-06-23T14:14:27.890204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
072
 
7.3%
238.45
 
0.5%
231.94
 
0.4%
105.64
 
0.4%
211.53
 
0.3%
192.83
 
0.3%
220.93
 
0.3%
2143
 
0.3%
234.23
 
0.3%
233.72
 
0.2%
Other values (154)173
 
17.5%
(Missing)713
72.2%
ValueCountFrequency (%)
072
7.3%
102.81
 
0.1%
102.91
 
0.1%
105.64
 
0.4%
106.11
 
0.1%
106.32
 
0.2%
106.91
 
0.1%
123.11
 
0.1%
123.51
 
0.1%
124.21
 
0.1%
ValueCountFrequency (%)
312.7881
0.1%
308.9761
0.1%
307.2261
0.1%
304.9691
0.1%
304.1471
0.1%
303.8371
0.1%
296.3221
0.1%
292.4421
0.1%
291.621
0.1%
291.3481
0.1%

WPU0915021625
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct88
Distinct (%)66.7%
Missing856
Missing (%)86.6%
Infinite0
Infinite (%)0.0%
Mean108.8670758
Minimum99.5
Maximum143.787
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:28.093313image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum99.5
5-th percentile100.965
Q1103
median105
Q3107.525
95-th percentile135.07855
Maximum143.787
Range44.287
Interquartile range (IQR)4.525

Descriptive statistics

Standard deviation10.68807037
Coefficient of variation (CV)0.09817541521
Kurtosis3.175015139
Mean108.8670758
Median Absolute Deviation (MAD)2.2
Skewness2.070073789
Sum14370.454
Variance114.2348481
MonotonicityNot monotonic
2023-06-23T14:14:28.280815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104.85
 
0.5%
104.54
 
0.4%
1034
 
0.4%
103.64
 
0.4%
105.34
 
0.4%
1054
 
0.4%
107.43
 
0.3%
102.93
 
0.3%
104.92
 
0.2%
105.82
 
0.2%
Other values (78)97
 
9.8%
(Missing)856
86.6%
ValueCountFrequency (%)
99.51
0.1%
99.91
0.1%
1001
0.1%
100.21
0.1%
100.61
0.1%
100.71
0.1%
100.81
0.1%
101.11
0.1%
101.22
0.2%
101.41
0.1%
ValueCountFrequency (%)
143.7871
0.1%
143.7021
0.1%
143.4721
0.1%
142.1661
0.1%
139.0781
0.1%
138.3811
0.1%
137.7841
0.1%
132.8651
0.1%
132.2161
0.1%
130.4971
0.1%

PCU3252132521
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct197
Distinct (%)86.4%
Missing760
Missing (%)76.9%
Infinite0
Infinite (%)0.0%
Mean164.0359079
Minimum100
Maximum231.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:28.468287image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile130.405
Q1145
median165.55
Q3176.95
95-th percentile220.1934
Maximum231.26
Range131.26
Interquartile range (IQR)31.95

Descriptive statistics

Standard deviation25.56231589
Coefficient of variation (CV)0.15583366
Kurtosis0.8337420438
Mean164.0359079
Median Absolute Deviation (MAD)13.15
Skewness0.4253957577
Sum37400.187
Variance653.4319937
MonotonicityNot monotonic
2023-06-23T14:14:28.671400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
155.74
 
0.4%
167.83
 
0.3%
178.73
 
0.3%
138.93
 
0.3%
167.43
 
0.3%
141.12
 
0.2%
179.42
 
0.2%
155.92
 
0.2%
172.32
 
0.2%
168.12
 
0.2%
Other values (187)202
 
20.4%
(Missing)760
76.9%
ValueCountFrequency (%)
1001
0.1%
102.31
0.1%
104.51
0.1%
105.51
0.1%
107.81
0.1%
108.71
0.1%
111.61
0.1%
113.51
0.1%
116.91
0.1%
121.71
0.1%
ValueCountFrequency (%)
231.261
0.1%
230.0931
0.1%
228.9481
0.1%
228.7231
0.1%
228.2551
0.1%
227.9191
0.1%
227.5751
0.1%
225.1541
0.1%
224.2631
0.1%
222.8511
0.1%

MHHNGSP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct220
Distinct (%)79.7%
Missing712
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean4.515036232
Minimum1.63
Maximum13.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:28.858888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.63
5-th percentile2.0725
Q12.8775
median3.965
Q35.735
95-th percentile8.33
Maximum13.42
Range11.79
Interquartile range (IQR)2.8575

Descriptive statistics

Standard deviation2.171598238
Coefficient of variation (CV)0.4809702794
Kurtosis2.260760422
Mean4.515036232
Median Absolute Deviation (MAD)1.26
Skewness1.371643507
Sum1246.15
Variance4.715838908
MonotonicityNot monotonic
2023-06-23T14:14:29.062016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.984
 
0.4%
2.994
 
0.4%
3.433
 
0.3%
4.93
 
0.3%
1.923
 
0.3%
2.663
 
0.3%
2.853
 
0.3%
4.043
 
0.3%
2.823
 
0.3%
3.262
 
0.2%
Other values (210)245
 
24.8%
(Missing)712
72.1%
ValueCountFrequency (%)
1.631
 
0.1%
1.731
 
0.1%
1.741
 
0.1%
1.751
 
0.1%
1.771
 
0.1%
1.791
 
0.1%
1.911
 
0.1%
1.923
0.3%
1.931
 
0.1%
1.951
 
0.1%
ValueCountFrequency (%)
13.421
0.1%
13.051
0.1%
12.691
0.1%
11.751
0.1%
11.271
0.1%
11.091
0.1%
10.31
0.1%
10.181
0.1%
9.531
0.1%
9.411
0.1%

WPU072205011
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct82
Distinct (%)71.3%
Missing873
Missing (%)88.4%
Infinite0
Infinite (%)0.0%
Mean106.6189565
Minimum94.1
Maximum138.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:29.249507image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum94.1
5-th percentile95.5
Q199.7
median102
Q3106.25
95-th percentile135.4996
Maximum138.09
Range43.99
Interquartile range (IQR)6.55

Descriptive statistics

Standard deviation11.89377604
Coefficient of variation (CV)0.1115540466
Kurtosis1.176088729
Mean106.6189565
Median Absolute Deviation (MAD)3.2
Skewness1.579048271
Sum12261.18
Variance141.4619086
MonotonicityNot monotonic
2023-06-23T14:14:29.421370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104.85
 
0.5%
100.85
 
0.5%
102.44
 
0.4%
105.33
 
0.3%
104.33
 
0.3%
95.53
 
0.3%
101.82
 
0.2%
1002
 
0.2%
99.22
 
0.2%
99.62
 
0.2%
Other values (72)84
 
8.5%
(Missing)873
88.4%
ValueCountFrequency (%)
94.11
 
0.1%
94.41
 
0.1%
94.51
 
0.1%
951
 
0.1%
95.53
0.3%
96.31
 
0.1%
96.71
 
0.1%
97.12
0.2%
97.21
 
0.1%
97.51
 
0.1%
ValueCountFrequency (%)
138.091
0.1%
137.791
0.1%
137.6371
0.1%
137.3161
0.1%
135.8731
0.1%
135.5781
0.1%
135.4661
0.1%
132.3991
0.1%
131.0031
0.1%
130.8051
0.1%

PCU32611132611115
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct85
Distinct (%)73.9%
Missing873
Missing (%)88.4%
Infinite0
Infinite (%)0.0%
Mean135.4470261
Minimum100
Maximum179.097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:29.608858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile124.8
Q1126.9
median130.2
Q3133.05
95-th percentile171.6727
Maximum179.097
Range79.097
Interquartile range (IQR)6.15

Descriptive statistics

Standard deviation15.06752601
Coefficient of variation (CV)0.1112429445
Kurtosis1.991551113
Mean135.4470261
Median Absolute Deviation (MAD)3.2
Skewness1.62718673
Sum15576.408
Variance227.0303399
MonotonicityNot monotonic
2023-06-23T14:14:29.796345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.84
 
0.4%
127.63
 
0.3%
1303
 
0.3%
131.33
 
0.3%
130.23
 
0.3%
126.93
 
0.3%
126.42
 
0.2%
125.52
 
0.2%
129.12
 
0.2%
128.52
 
0.2%
Other values (75)88
 
8.9%
(Missing)873
88.4%
ValueCountFrequency (%)
1001
0.1%
123.31
0.1%
123.81
0.1%
123.91
0.1%
124.11
0.1%
124.82
0.2%
124.92
0.2%
1252
0.2%
125.11
0.1%
125.21
0.1%
ValueCountFrequency (%)
179.0971
0.1%
178.921
0.1%
178.7221
0.1%
177.3991
0.1%
177.2421
0.1%
172.3371
0.1%
171.3881
0.1%
170.7661
0.1%
164.421
0.1%
163.61
0.1%

PCU32611332611301.1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct164
Distinct (%)80.4%
Missing784
Missing (%)79.4%
Infinite0
Infinite (%)0.0%
Mean201.2436863
Minimum102.8
Maximum311.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:29.999435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum102.8
5-th percentile123.16
Q1152.8
median215.6
Q3231.9
95-th percentile290.967
Maximum311.13
Range208.33
Interquartile range (IQR)79.1

Descriptive statistics

Standard deviation50.81902388
Coefficient of variation (CV)0.2525248112
Kurtosis-0.5279789779
Mean201.2436863
Median Absolute Deviation (MAD)22.8
Skewness-0.1309503337
Sum41053.712
Variance2582.573188
MonotonicityNot monotonic
2023-06-23T14:14:30.186939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
238.45
 
0.5%
231.94
 
0.4%
105.64
 
0.4%
211.53
 
0.3%
2143
 
0.3%
234.23
 
0.3%
220.93
 
0.3%
192.83
 
0.3%
192.92
 
0.2%
219.12
 
0.2%
Other values (154)172
 
17.4%
(Missing)784
79.4%
ValueCountFrequency (%)
102.81
 
0.1%
102.91
 
0.1%
105.64
0.4%
106.11
 
0.1%
106.32
0.2%
106.91
 
0.1%
123.11
 
0.1%
123.51
 
0.1%
124.21
 
0.1%
126.81
 
0.1%
ValueCountFrequency (%)
311.131
0.1%
310.2961
0.1%
309.8691
0.1%
308.9761
0.1%
304.9691
0.1%
304.1471
0.1%
303.8371
0.1%
296.3221
0.1%
292.4421
0.1%
291.621
0.1%

PCU32611132611112
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct87
Distinct (%)75.7%
Missing873
Missing (%)88.4%
Infinite0
Infinite (%)0.0%
Mean128.0431391
Minimum100
Maximum168.906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:30.515028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile115.87
Q1120
median123.1
Q3127.15
95-th percentile160.0259
Maximum168.906
Range68.906
Interquartile range (IQR)7.15

Descriptive statistics

Standard deviation14.14634723
Coefficient of variation (CV)0.1104811029
Kurtosis1.276457264
Mean128.0431391
Median Absolute Deviation (MAD)3.7
Skewness1.523872227
Sum14724.961
Variance200.11914
MonotonicityNot monotonic
2023-06-23T14:14:30.702516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.66
 
0.6%
121.43
 
0.3%
124.53
 
0.3%
1192
 
0.2%
151.4052
 
0.2%
123.12
 
0.2%
117.62
 
0.2%
119.42
 
0.2%
1282
 
0.2%
118.62
 
0.2%
Other values (77)89
 
9.0%
(Missing)873
88.4%
ValueCountFrequency (%)
1001
0.1%
114.31
0.1%
114.71
0.1%
115.11
0.1%
115.71
0.1%
115.81
0.1%
115.91
0.1%
117.31
0.1%
117.42
0.2%
117.62
0.2%
ValueCountFrequency (%)
168.9061
0.1%
167.2681
0.1%
164.0931
0.1%
161.6531
0.1%
161.281
0.1%
161.2531
0.1%
159.52
0.2%
158.6631
0.1%
157.8011
0.1%
157.3621
0.1%

WPU0915021622
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct101
Distinct (%)75.9%
Missing855
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean111.1839925
Minimum99.2
Maximum147.624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:30.890001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum99.2
5-th percentile100.16
Q1104.1
median107.2
Q3111.4
95-th percentile140.297
Maximum147.624
Range48.424
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation12.07077583
Coefficient of variation (CV)0.1085657707
Kurtosis1.78781616
Mean111.1839925
Median Absolute Deviation (MAD)3.7
Skewness1.706273315
Sum14787.471
Variance145.7036292
MonotonicityNot monotonic
2023-06-23T14:14:31.077510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
106.27
 
0.7%
106.83
 
0.3%
100.23
 
0.3%
105.83
 
0.3%
109.73
 
0.3%
103.62
 
0.2%
104.12
 
0.2%
107.22
 
0.2%
140.2972
 
0.2%
109.42
 
0.2%
Other values (91)104
 
10.5%
(Missing)855
86.5%
ValueCountFrequency (%)
99.21
 
0.1%
99.61
 
0.1%
99.72
0.2%
99.81
 
0.1%
1001
 
0.1%
100.11
 
0.1%
100.23
0.3%
100.51
 
0.1%
100.71
 
0.1%
100.81
 
0.1%
ValueCountFrequency (%)
147.6241
0.1%
146.3481
0.1%
143.8751
0.1%
141.9731
0.1%
141.6621
0.1%
141.4131
0.1%
140.2972
0.2%
139.6441
0.1%
138.9721
0.1%
138.3611
0.1%

Producer Price Index by Industry: Plastics Material and Resins Manufacturing: Thermoplastic Resins and Plastics Materials
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct239
Distinct (%)86.6%
Missing712
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean222.319692
Minimum122.9
Maximum359.606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.8 KiB
2023-06-23T14:14:31.264996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum122.9
5-th percentile140.5
Q1193.375
median227.55
Q3249
95-th percentile328.7595
Maximum359.606
Range236.706
Interquartile range (IQR)55.625

Descriptive statistics

Standard deviation51.94150844
Coefficient of variation (CV)0.2336343127
Kurtosis0.2270444881
Mean222.319692
Median Absolute Deviation (MAD)26.35
Skewness0.2594052577
Sum61360.235
Variance2697.920299
MonotonicityNot monotonic
2023-06-23T14:14:31.452481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210.22
 
0.2%
218.72
 
0.2%
258.42
 
0.2%
222.62
 
0.2%
245.52
 
0.2%
242.52
 
0.2%
210.32
 
0.2%
248.72
 
0.2%
1892
 
0.2%
244.52
 
0.2%
Other values (229)256
 
25.9%
(Missing)712
72.1%
ValueCountFrequency (%)
122.91
0.1%
123.51
0.1%
125.41
0.1%
1261
0.1%
128.71
0.1%
129.11
0.1%
129.31
0.1%
131.11
0.1%
131.71
0.1%
134.11
0.1%
ValueCountFrequency (%)
359.6061
0.1%
358.4541
0.1%
358.011
0.1%
353.751
0.1%
353.3361
0.1%
352.1121
0.1%
349.3191
0.1%
343.3951
0.1%
343.3271
0.1%
342.71
0.1%

Australia _export
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct38
Distinct (%)100.0%
Missing950
Missing (%)96.2%
Memory size7.8 KiB
3,33,023
 
1
4,88,137
 
1
19,69,610
 
1
3,54,349
 
1
1,70,305
 
1
Other values (33)
33 

Length

Max length9
Median length8.5
Mean length8.5
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st row4,88,137
2nd row13,57,191
3rd row3,33,023
4th row3,82,043
5th row13,21,791

Common Values

ValueCountFrequency (%)
3,33,0231
 
0.1%
4,88,1371
 
0.1%
19,69,6101
 
0.1%
3,54,3491
 
0.1%
1,70,3051
 
0.1%
15,21,5261
 
0.1%
4,57,6371
 
0.1%
15,37,5071
 
0.1%
29,05,9051
 
0.1%
91,244.001
 
0.1%
Other values (28)28
 
2.8%
(Missing)950
96.2%

Length

2023-06-23T14:14:31.639952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,33,0231
 
2.6%
2,55,3711
 
2.6%
2,00,1991
 
2.6%
12,84,7681
 
2.6%
30,02,8801
 
2.6%
1,85,9531
 
2.6%
6,96,7371
 
2.6%
10,62,2351
 
2.6%
10,99,9951
 
2.6%
4,55,0411
 
2.6%
Other values (28)28
73.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Canada_export
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct154
Distinct (%)100.0%
Missing834
Missing (%)84.4%
Memory size7.8 KiB
14,05,16,694.00
 
1
12,09,79,267.00
 
1
17,14,81,945.00
 
1
13,06,08,582.00
 
1
10,22,86,951.00
 
1
Other values (149)
149 

Length

Max length15
Median length15
Mean length14.7987013
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154 ?
Unique (%)100.0%

Sample

1st row8,52,64,272.00
2nd row8,40,61,885.00
3rd row9,18,95,503.00
4th row7,69,73,466.00
5th row9,54,54,363.00

Common Values

ValueCountFrequency (%)
14,05,16,694.001
 
0.1%
12,09,79,267.001
 
0.1%
17,14,81,945.001
 
0.1%
13,06,08,582.001
 
0.1%
10,22,86,951.001
 
0.1%
8,67,86,839.001
 
0.1%
14,16,58,623.001
 
0.1%
11,92,29,537.001
 
0.1%
13,90,26,477.001
 
0.1%
14,27,26,808.001
 
0.1%
Other values (144)144
 
14.6%
(Missing)834
84.4%

Length

2023-06-23T14:14:31.796191image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14,05,16,694.001
 
0.6%
14,61,05,282.001
 
0.6%
13,55,32,173.001
 
0.6%
11,77,32,187.001
 
0.6%
14,21,30,602.001
 
0.6%
18,52,25,434.001
 
0.6%
13,24,69,600.001
 
0.6%
9,56,26,904.001
 
0.6%
11,83,99,936.001
 
0.6%
14,32,56,927.001
 
0.6%
Other values (144)144
93.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Saudi_export
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct33
Distinct (%)100.0%
Missing955
Missing (%)96.7%
Memory size7.8 KiB
33,69,66,821
 
1
38,49,89,281
 
1
40,04,31,700
 
1
30,00,31,308
 
1
32,75,82,318
 
1
Other values (28)
28 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st row44,61,42,297
2nd row39,80,50,674
3rd row39,36,52,269
4th row36,41,37,790
5th row39,53,76,495

Common Values

ValueCountFrequency (%)
33,69,66,8211
 
0.1%
38,49,89,2811
 
0.1%
40,04,31,7001
 
0.1%
30,00,31,3081
 
0.1%
32,75,82,3181
 
0.1%
35,78,73,5141
 
0.1%
49,43,45,9511
 
0.1%
40,74,02,0311
 
0.1%
38,66,87,8151
 
0.1%
40,96,18,7031
 
0.1%
Other values (23)23
 
2.3%
(Missing)955
96.7%

Length

2023-06-23T14:14:31.952431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
33,69,66,8211
 
3.0%
39,80,50,6741
 
3.0%
42,70,54,4841
 
3.0%
45,80,15,8851
 
3.0%
36,51,15,9231
 
3.0%
39,36,52,2691
 
3.0%
31,74,92,1301
 
3.0%
38,34,69,4461
 
3.0%
35,42,39,3761
 
3.0%
37,05,68,7291
 
3.0%
Other values (23)23
69.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Usa_export
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct154
Distinct (%)100.0%
Missing834
Missing (%)84.4%
Memory size7.8 KiB
21,28,36,673.00
 
1
22,38,22,695.00
 
1
39,69,77,349.00
 
1
25,99,64,470.00
 
1
19,36,46,672.00
 
1
Other values (149)
149 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154 ?
Unique (%)100.0%

Sample

1st row24,36,08,493.00
2nd row23,01,78,732.00
3rd row23,24,68,187.00
4th row16,80,11,033.00
5th row16,12,34,089.00

Common Values

ValueCountFrequency (%)
21,28,36,673.001
 
0.1%
22,38,22,695.001
 
0.1%
39,69,77,349.001
 
0.1%
25,99,64,470.001
 
0.1%
19,36,46,672.001
 
0.1%
20,70,62,350.001
 
0.1%
40,17,90,570.001
 
0.1%
36,87,26,811.001
 
0.1%
24,52,69,929.001
 
0.1%
25,65,57,083.001
 
0.1%
Other values (144)144
 
14.6%
(Missing)834
84.4%

Length

2023-06-23T14:14:32.093045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
21,28,36,673.001
 
0.6%
30,24,20,633.001
 
0.6%
23,79,13,637.001
 
0.6%
22,89,42,126.001
 
0.6%
35,92,59,049.001
 
0.6%
24,75,56,849.001
 
0.6%
44,82,42,158.001
 
0.6%
25,19,75,816.001
 
0.6%
18,82,38,250.001
 
0.6%
51,79,68,858.001
 
0.6%
Other values (144)144
93.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

India_export
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct142
Distinct (%)100.0%
Missing846
Missing (%)85.6%
Memory size7.8 KiB
1,76,45,498.00
 
1
2,51,98,524.00
 
1
1,00,33,898.00
 
1
20,62,662.00
 
1
2,87,74,251.00
 
1
Other values (137)
137 

Length

Max length14
Median length14
Mean length13.32394366
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)100.0%

Sample

1st row3,77,73,078.00
2nd row2,36,28,597.00
3rd row2,97,03,823.00
4th row3,10,24,430.00
5th row2,36,93,565.00

Common Values

ValueCountFrequency (%)
1,76,45,498.001
 
0.1%
2,51,98,524.001
 
0.1%
1,00,33,898.001
 
0.1%
20,62,662.001
 
0.1%
2,87,74,251.001
 
0.1%
4,44,77,407.001
 
0.1%
52,20,282.001
 
0.1%
1,16,16,153.001
 
0.1%
2,36,93,565.001
 
0.1%
1,38,66,269.001
 
0.1%
Other values (132)132
 
13.4%
(Missing)846
85.6%

Length

2023-06-23T14:14:32.264929image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,76,45,498.001
 
0.7%
2,03,09,130.001
 
0.7%
71,62,650.001
 
0.7%
1,05,49,347.001
 
0.7%
3,88,61,222.001
 
0.7%
5,87,77,728.001
 
0.7%
34,14,820.001
 
0.7%
2,46,09,360.001
 
0.7%
2,19,71,327.001
 
0.7%
1,20,71,230.001
 
0.7%
Other values (132)132
93.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Russia_export
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct108
Distinct (%)100.0%
Missing880
Missing (%)89.1%
Memory size7.8 KiB
1,40,49,891.00
 
1
2,62,86,525.00
 
1
3,01,03,894.00
 
1
1,15,11,986.00
 
1
1,73,21,417.00
 
1
Other values (103)
103 

Length

Max length15
Median length14
Mean length13.86111111
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)100.0%

Sample

1st row2,39,84,758.00
2nd row2,29,73,896.00
3rd row2,89,12,818.00
4th row3,20,58,101.00
5th row2,22,40,491.00

Common Values

ValueCountFrequency (%)
1,40,49,891.001
 
0.1%
2,62,86,525.001
 
0.1%
3,01,03,894.001
 
0.1%
1,15,11,986.001
 
0.1%
1,73,21,417.001
 
0.1%
1,10,42,468.001
 
0.1%
1,11,34,368.001
 
0.1%
1,27,52,645.001
 
0.1%
1,90,10,413.001
 
0.1%
1,65,17,744.001
 
0.1%
Other values (98)98
 
9.9%
(Missing)880
89.1%

Length

2023-06-23T14:14:32.436795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,40,49,891.001
 
0.9%
4,58,84,837.001
 
0.9%
2,39,84,758.001
 
0.9%
9,69,15,784.001
 
0.9%
1,09,89,958.001
 
0.9%
1,19,45,694.001
 
0.9%
5,89,83,037.001
 
0.9%
7,23,79,596.001
 
0.9%
91,47,850.001
 
0.9%
1,31,01,988.001
 
0.9%
Other values (98)98
90.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

South_Africa_export
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct154
Distinct (%)100.0%
Missing834
Missing (%)84.4%
Memory size7.8 KiB
8,05,536.00
 
1
26,72,614.00
 
1
26,66,913.00
 
1
20,76,096.00
 
1
24,64,090.00
 
1
Other values (149)
149 

Length

Max length12
Median length12
Mean length11.95454545
Min length11

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154 ?
Unique (%)100.0%

Sample

1st row8,05,536.00
2nd row18,04,158.00
3rd row14,24,084.00
4th row9,14,269.00
5th row9,00,239.00

Common Values

ValueCountFrequency (%)
8,05,536.001
 
0.1%
26,72,614.001
 
0.1%
26,66,913.001
 
0.1%
20,76,096.001
 
0.1%
24,64,090.001
 
0.1%
11,19,934.001
 
0.1%
18,68,385.001
 
0.1%
20,76,855.001
 
0.1%
16,82,993.001
 
0.1%
20,51,348.001
 
0.1%
Other values (144)144
 
14.6%
(Missing)834
84.4%

Length

2023-06-23T14:14:32.593034image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8,05,536.001
 
0.6%
26,38,636.001
 
0.6%
49,79,994.001
 
0.6%
42,16,186.001
 
0.6%
21,74,737.001
 
0.6%
20,53,072.001
 
0.6%
9,14,269.001
 
0.6%
22,78,087.001
 
0.6%
23,88,281.001
 
0.6%
32,25,187.001
 
0.6%
Other values (144)144
93.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Turkey
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct272
Distinct (%)100.0%
Missing716
Missing (%)72.5%
Memory size7.8 KiB
14,23,330.00
 
1
20,52,901.00
 
1
1,03,183.00
 
1
1,24,62,762.00
 
1
11,06,185.00
 
1
Other values (267)
267 

Length

Max length14
Median length12
Mean length11.52573529
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique272 ?
Unique (%)100.0%

Sample

1st row61,808.00
2nd row4,50,675.00
3rd row6,71,273.00
4th row73,178.00
5th row2,11,953.00

Common Values

ValueCountFrequency (%)
14,23,330.001
 
0.1%
20,52,901.001
 
0.1%
1,03,183.001
 
0.1%
1,24,62,762.001
 
0.1%
11,06,185.001
 
0.1%
82,929.001
 
0.1%
7,75,188.001
 
0.1%
33,08,377.001
 
0.1%
3,15,187.001
 
0.1%
30,89,686.001
 
0.1%
Other values (262)262
 
26.5%
(Missing)716
72.5%

Length

2023-06-23T14:14:32.764877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14,23,330.001
 
0.4%
46,96,117.001
 
0.4%
1,88,435.001
 
0.4%
29,46,223.001
 
0.4%
27,36,783.001
 
0.4%
74,906.001
 
0.4%
7,80,248.001
 
0.4%
39,00,157.001
 
0.4%
1,97,83,404.001
 
0.4%
20,52,901.001
 
0.4%
Other values (262)262
96.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Brazil
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct275
Distinct (%)100.0%
Missing713
Missing (%)72.2%
Memory size7.8 KiB
3,14,15,738.00
 
1
2,46,55,884.00
 
1
1,58,52,411.00
 
1
4,64,38,342.00
 
1
4,64,90,244.00
 
1
Other values (270)
270 

Length

Max length14
Median length14
Mean length13.94181818
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique275 ?
Unique (%)100.0%

Sample

1st row1,03,52,529.00
2nd row88,44,172.00
3rd row1,40,91,209.00
4th row1,40,97,518.00
5th row1,67,62,995.00

Common Values

ValueCountFrequency (%)
3,14,15,738.001
 
0.1%
2,46,55,884.001
 
0.1%
1,58,52,411.001
 
0.1%
4,64,38,342.001
 
0.1%
4,64,90,244.001
 
0.1%
3,34,75,473.001
 
0.1%
3,18,62,428.001
 
0.1%
3,29,10,392.001
 
0.1%
3,19,55,967.001
 
0.1%
3,87,48,175.001
 
0.1%
Other values (265)265
 
26.8%
(Missing)713
72.2%

Length

2023-06-23T14:14:32.952367image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,14,15,738.001
 
0.4%
2,00,86,691.001
 
0.4%
4,84,03,337.001
 
0.4%
2,63,36,656.001
 
0.4%
1,10,75,284.001
 
0.4%
4,98,42,724.001
 
0.4%
2,69,97,797.001
 
0.4%
3,83,49,932.001
 
0.4%
2,11,82,469.001
 
0.4%
2,64,06,150.001
 
0.4%
Other values (265)265
96.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

France_export
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct240
Distinct (%)100.0%
Missing748
Missing (%)75.7%
Memory size7.8 KiB
5,56,72,219.00
 
1
2,93,96,901.00
 
1
4,96,51,661.00
 
1
2,34,74,148.00
 
1
4,15,02,280.00
 
1
Other values (235)
235 

Length

Max length14
Median length14
Mean length13.99166667
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique240 ?
Unique (%)100.0%

Sample

1st row2,93,27,224.00
2nd row3,35,06,035.00
3rd row3,57,64,935.00
4th row2,47,13,057.00
5th row2,84,13,226.00

Common Values

ValueCountFrequency (%)
5,56,72,219.001
 
0.1%
2,93,96,901.001
 
0.1%
4,96,51,661.001
 
0.1%
2,34,74,148.001
 
0.1%
4,15,02,280.001
 
0.1%
2,17,35,421.001
 
0.1%
3,35,06,035.001
 
0.1%
4,49,30,538.001
 
0.1%
4,31,51,140.001
 
0.1%
3,85,10,257.001
 
0.1%
Other values (230)230
 
23.3%
(Missing)748
75.7%

Length

2023-06-23T14:14:33.124231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5,56,72,219.001
 
0.4%
2,93,96,901.001
 
0.4%
6,12,89,514.001
 
0.4%
4,64,52,301.001
 
0.4%
2,65,91,305.001
 
0.4%
3,57,19,908.001
 
0.4%
4,70,60,390.001
 
0.4%
2,57,45,806.001
 
0.4%
5,20,80,370.001
 
0.4%
6,16,50,186.001
 
0.4%
Other values (230)230
95.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Germeny_export
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct178
Distinct (%)100.0%
Missing810
Missing (%)82.0%
Memory size7.8 KiB
14,38,46,973.00
 
1
14,96,93,972.00
 
1
13,04,04,272.00
 
1
11,40,60,230.00
 
1
16,83,24,939.00
 
1
Other values (173)
173 

Length

Max length15
Median length15
Mean length14.92696629
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)100.0%

Sample

1st row16,66,51,741.00
2nd row16,65,53,129.00
3rd row17,70,37,039.00
4th row19,05,67,343.00
5th row16,43,06,225.00

Common Values

ValueCountFrequency (%)
14,38,46,973.001
 
0.1%
14,96,93,972.001
 
0.1%
13,04,04,272.001
 
0.1%
11,40,60,230.001
 
0.1%
16,83,24,939.001
 
0.1%
10,87,09,091.001
 
0.1%
13,15,37,544.001
 
0.1%
14,55,67,523.001
 
0.1%
7,45,84,907.001
 
0.1%
10,62,65,734.001
 
0.1%
Other values (168)168
 
17.0%
(Missing)810
82.0%

Length

2023-06-23T14:14:33.296111image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14,38,46,973.001
 
0.6%
13,29,47,791.001
 
0.6%
11,44,28,227.001
 
0.6%
13,99,38,790.001
 
0.6%
12,81,45,165.001
 
0.6%
14,88,08,996.001
 
0.6%
12,23,38,709.001
 
0.6%
15,60,12,909.001
 
0.6%
9,36,85,189.001
 
0.6%
13,02,81,209.001
 
0.6%
Other values (168)168
94.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

United Kingdome_export
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct153
Distinct (%)100.0%
Missing835
Missing (%)84.5%
Memory size7.8 KiB
3,95,86,301.00
 
1
3,90,89,565.00
 
1
4,00,97,406.00
 
1
6,22,62,526.00
 
1
3,53,73,852.00
 
1
Other values (148)
148 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique153 ?
Unique (%)100.0%

Sample

1st row4,46,66,643.00
2nd row4,29,28,511.00
3rd row4,12,44,447.00
4th row3,83,25,803.00
5th row4,78,62,867.00

Common Values

ValueCountFrequency (%)
3,95,86,301.001
 
0.1%
3,90,89,565.001
 
0.1%
4,00,97,406.001
 
0.1%
6,22,62,526.001
 
0.1%
3,53,73,852.001
 
0.1%
2,71,99,149.001
 
0.1%
3,89,16,596.001
 
0.1%
3,82,60,684.001
 
0.1%
3,88,70,675.001
 
0.1%
4,61,70,028.001
 
0.1%
Other values (143)143
 
14.5%
(Missing)835
84.5%

Length

2023-06-23T14:14:33.467957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3,95,86,301.001
 
0.7%
3,22,07,825.001
 
0.7%
1,95,68,806.001
 
0.7%
3,89,17,543.001
 
0.7%
4,01,14,550.001
 
0.7%
3,61,08,326.001
 
0.7%
3,48,66,087.001
 
0.7%
4,12,44,447.001
 
0.7%
6,38,97,507.001
 
0.7%
3,15,86,728.001
 
0.7%
Other values (143)143
93.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

China_export
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct108
Distinct (%)100.0%
Missing880
Missing (%)89.1%
Memory size7.8 KiB
1,03,10,583.00
 
1
5,64,55,376.00
 
1
1,71,06,472.00
 
1
1,51,16,555.00
 
1
38,16,326.00
 
1
Other values (103)
103 

Length

Max length14
Median length14
Mean length13.64814815
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)100.0%

Sample

1st row27,53,319.00
2nd row18,43,728.00
3rd row85,22,552.00
4th row1,71,17,147.00
5th row1,15,82,030.00

Common Values

ValueCountFrequency (%)
1,03,10,583.001
 
0.1%
5,64,55,376.001
 
0.1%
1,71,06,472.001
 
0.1%
1,51,16,555.001
 
0.1%
38,16,326.001
 
0.1%
97,83,312.001
 
0.1%
1,91,72,440.001
 
0.1%
2,98,73,315.001
 
0.1%
1,76,11,892.001
 
0.1%
1,62,25,620.001
 
0.1%
Other values (98)98
 
9.9%
(Missing)880
89.1%

Length

2023-06-23T14:14:33.639822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,03,10,583.001
 
0.9%
2,11,55,955.001
 
0.9%
2,15,31,912.001
 
0.9%
1,08,63,742.001
 
0.9%
2,01,18,728.001
 
0.9%
6,08,89,195.001
 
0.9%
1,47,72,728.001
 
0.9%
2,23,77,566.001
 
0.9%
1,16,50,612.001
 
0.9%
2,02,34,715.001
 
0.9%
Other values (98)98
90.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Australia _import
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct109
Distinct (%)100.0%
Missing879
Missing (%)89.0%
Memory size7.8 KiB
1,37,97,788.00
 
1
1,53,50,100.00
 
1
1,52,48,529.00
 
1
2,63,25,891.00
 
1
87,83,105.00
 
1
Other values (104)
104 

Length

Max length14
Median length14
Mean length13.77981651
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)100.0%

Sample

1st row1,19,62,404.00
2nd row22,18,717.00
3rd row66,00,377.00
4th row83,40,579.00
5th row69,03,302.00

Common Values

ValueCountFrequency (%)
1,37,97,788.001
 
0.1%
1,53,50,100.001
 
0.1%
1,52,48,529.001
 
0.1%
2,63,25,891.001
 
0.1%
87,83,105.001
 
0.1%
1,39,35,746.001
 
0.1%
91,66,871.001
 
0.1%
3,44,32,342.001
 
0.1%
2,77,68,083.001
 
0.1%
83,40,579.001
 
0.1%
Other values (99)99
 
10.0%
(Missing)879
89.0%

Length

2023-06-23T14:14:33.811686image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,37,97,788.001
 
0.9%
44,96,510.001
 
0.9%
1,72,31,107.001
 
0.9%
1,81,01,116.001
 
0.9%
2,80,66,968.001
 
0.9%
2,22,51,076.001
 
0.9%
1,60,37,911.001
 
0.9%
1,62,61,371.001
 
0.9%
1,44,38,022.001
 
0.9%
1,38,27,313.001
 
0.9%
Other values (99)99
90.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Canada_import
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct154
Distinct (%)100.0%
Missing834
Missing (%)84.4%
Memory size7.8 KiB
2,57,87,263.00
 
1
6,84,11,546.00
 
1
4,55,88,674.00
 
1
3,87,20,042.00
 
1
3,99,09,408.00
 
1
Other values (149)
149 

Length

Max length15
Median length14
Mean length14.00649351
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154 ?
Unique (%)100.0%

Sample

1st row2,57,87,263.00
2nd row2,86,43,366.00
3rd row3,83,97,083.00
4th row3,12,99,102.00
5th row3,48,79,377.00

Common Values

ValueCountFrequency (%)
2,57,87,263.001
 
0.1%
6,84,11,546.001
 
0.1%
4,55,88,674.001
 
0.1%
3,87,20,042.001
 
0.1%
3,99,09,408.001
 
0.1%
4,10,60,728.001
 
0.1%
3,71,80,562.001
 
0.1%
5,44,59,039.001
 
0.1%
4,08,34,224.001
 
0.1%
4,45,60,733.001
 
0.1%
Other values (144)144
 
14.6%
(Missing)834
84.4%

Length

2023-06-23T14:14:33.983567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2,57,87,263.001
 
0.6%
4,11,51,852.001
 
0.6%
3,20,99,690.001
 
0.6%
4,47,61,483.001
 
0.6%
4,10,30,383.001
 
0.6%
3,48,79,377.001
 
0.6%
6,89,07,927.001
 
0.6%
3,61,07,115.001
 
0.6%
5,87,01,009.001
 
0.6%
3,86,62,662.001
 
0.6%
Other values (144)144
93.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Saudi_import
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct33
Distinct (%)100.0%
Missing955
Missing (%)96.7%
Memory size7.8 KiB
44,52,005.00
 
1
89,65,786.00
 
1
37,43,199.00
 
1
38,68,004.00
 
1
27,44,536.00
 
1
Other values (28)
28 

Length

Max length14
Median length12
Mean length12.06060606
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st row85,66,216.00
2nd row79,22,253.00
3rd row1,08,74,862.00
4th row61,95,076.00
5th row89,65,786.00

Common Values

ValueCountFrequency (%)
44,52,005.001
 
0.1%
89,65,786.001
 
0.1%
37,43,199.001
 
0.1%
38,68,004.001
 
0.1%
27,44,536.001
 
0.1%
91,95,047.001
 
0.1%
77,16,009.001
 
0.1%
83,14,143.001
 
0.1%
85,20,544.001
 
0.1%
44,53,605.001
 
0.1%
Other values (23)23
 
2.3%
(Missing)955
96.7%

Length

2023-06-23T14:14:34.155430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
44,52,005.001
 
3.0%
75,25,342.001
 
3.0%
73,08,351.001
 
3.0%
40,93,065.001
 
3.0%
58,70,131.001
 
3.0%
66,88,798.001
 
3.0%
90,66,203.001
 
3.0%
48,43,465.001
 
3.0%
78,30,943.001
 
3.0%
44,42,672.001
 
3.0%
Other values (23)23
69.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Usa_import
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct154
Distinct (%)100.0%
Missing834
Missing (%)84.4%
Memory size7.8 KiB
11,69,25,749.00
 
1
10,58,35,082.00
 
1
10,41,04,567.00
 
1
7,46,61,951.00
 
1
13,26,01,647.00
 
1
Other values (149)
149 

Length

Max length15
Median length15
Mean length14.86363636
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154 ?
Unique (%)100.0%

Sample

1st row8,28,77,181.00
2nd row7,83,72,596.00
3rd row8,96,50,913.00
4th row7,46,61,951.00
5th row9,53,86,109.00

Common Values

ValueCountFrequency (%)
11,69,25,749.001
 
0.1%
10,58,35,082.001
 
0.1%
10,41,04,567.001
 
0.1%
7,46,61,951.001
 
0.1%
13,26,01,647.001
 
0.1%
13,95,74,962.001
 
0.1%
11,21,31,741.001
 
0.1%
10,45,51,390.001
 
0.1%
9,53,28,210.001
 
0.1%
14,25,68,654.001
 
0.1%
Other values (144)144
 
14.6%
(Missing)834
84.4%

Length

2023-06-23T14:14:34.452286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11,69,25,749.001
 
0.6%
11,67,51,172.001
 
0.6%
9,04,58,218.001
 
0.6%
13,38,31,956.001
 
0.6%
8,75,06,959.001
 
0.6%
9,53,86,109.001
 
0.6%
10,94,15,963.001
 
0.6%
11,62,80,282.001
 
0.6%
11,04,82,151.001
 
0.6%
12,01,57,084.001
 
0.6%
Other values (144)144
93.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

India_import
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct142
Distinct (%)100.0%
Missing846
Missing (%)85.6%
Memory size7.8 KiB
4,74,11,776.00
 
1
3,88,69,078.00
 
1
4,85,46,182.00
 
1
6,79,56,128.00
 
1
3,92,10,696.00
 
1
Other values (137)
137 

Length

Max length15
Median length14
Mean length14.04929577
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)100.0%

Sample

1st row3,56,49,297.00
2nd row2,80,57,324.00
3rd row3,24,57,153.00
4th row2,52,34,581.00
5th row3,19,93,874.00

Common Values

ValueCountFrequency (%)
4,74,11,776.001
 
0.1%
3,88,69,078.001
 
0.1%
4,85,46,182.001
 
0.1%
6,79,56,128.001
 
0.1%
3,92,10,696.001
 
0.1%
6,91,16,767.001
 
0.1%
3,32,40,787.001
 
0.1%
6,65,06,562.001
 
0.1%
2,80,57,324.001
 
0.1%
10,73,43,571.001
 
0.1%
Other values (132)132
 
13.4%
(Missing)846
85.6%

Length

2023-06-23T14:14:34.612276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,74,11,776.001
 
0.7%
4,43,56,313.001
 
0.7%
7,64,10,017.001
 
0.7%
7,41,98,543.001
 
0.7%
5,24,43,067.001
 
0.7%
7,19,28,350.001
 
0.7%
4,94,18,362.001
 
0.7%
5,87,80,036.001
 
0.7%
6,46,51,393.001
 
0.7%
6,57,97,053.001
 
0.7%
Other values (132)132
93.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Russia_import
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct108
Distinct (%)100.0%
Missing880
Missing (%)89.1%
Memory size7.8 KiB
5,29,56,277.00
 
1
3,51,07,469.00
 
1
4,03,86,156.00
 
1
4,35,39,074.00
 
1
2,21,68,831.00
 
1
Other values (103)
103 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)100.0%

Sample

1st row4,71,76,610.00
2nd row4,71,94,502.00
3rd row5,56,88,584.00
4th row5,29,56,277.00
5th row4,74,71,124.00

Common Values

ValueCountFrequency (%)
5,29,56,277.001
 
0.1%
3,51,07,469.001
 
0.1%
4,03,86,156.001
 
0.1%
4,35,39,074.001
 
0.1%
2,21,68,831.001
 
0.1%
4,83,43,448.001
 
0.1%
2,85,48,067.001
 
0.1%
3,09,66,580.001
 
0.1%
4,71,76,610.001
 
0.1%
1,81,51,172.001
 
0.1%
Other values (98)98
 
9.9%
(Missing)880
89.1%

Length

2023-06-23T14:14:34.768502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
5,29,56,277.001
 
0.9%
1,92,43,706.001
 
0.9%
2,55,21,764.001
 
0.9%
2,45,07,588.001
 
0.9%
3,26,28,796.001
 
0.9%
1,70,81,539.001
 
0.9%
2,51,26,526.001
 
0.9%
2,11,09,078.001
 
0.9%
2,13,94,676.001
 
0.9%
3,57,22,638.001
 
0.9%
Other values (98)98
90.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

South_Africa_import
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct154
Distinct (%)100.0%
Missing834
Missing (%)84.4%
Memory size7.8 KiB
1,08,71,547.00
 
1
80,08,851.00
 
1
1,02,90,579.00
 
1
1,30,58,392.00
 
1
1,58,24,064.00
 
1
Other values (149)
149 

Length

Max length14
Median length14
Mean length13.28571429
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154 ?
Unique (%)100.0%

Sample

1st row97,23,836.00
2nd row54,46,836.00
3rd row88,46,398.00
4th row97,71,332.00
5th row1,07,12,552.00

Common Values

ValueCountFrequency (%)
1,08,71,547.001
 
0.1%
80,08,851.001
 
0.1%
1,02,90,579.001
 
0.1%
1,30,58,392.001
 
0.1%
1,58,24,064.001
 
0.1%
1,20,83,334.001
 
0.1%
1,58,80,512.001
 
0.1%
1,53,02,192.001
 
0.1%
1,44,10,445.001
 
0.1%
74,19,895.001
 
0.1%
Other values (144)144
 
14.6%
(Missing)834
84.4%

Length

2023-06-23T14:14:34.924741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,08,71,547.001
 
0.6%
1,52,18,595.001
 
0.6%
1,22,09,958.001
 
0.6%
90,79,711.001
 
0.6%
98,99,792.001
 
0.6%
1,93,53,083.001
 
0.6%
1,02,40,296.001
 
0.6%
1,24,94,588.001
 
0.6%
78,89,495.001
 
0.6%
90,41,179.001
 
0.6%
Other values (144)144
93.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Turkey_import
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct168
Distinct (%)100.0%
Missing820
Missing (%)83.0%
Memory size7.8 KiB
4,41,10,571.00
 
1
1,26,01,747.00
 
1
11,92,48,730.00
 
1
4,78,43,891.00
 
1
7,14,03,356.00
 
1
Other values (163)
163 

Length

Max length15
Median length14
Mean length13.66071429
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)100.0%

Sample

1st row68,33,222.00
2nd row56,47,987.00
3rd row67,21,125.00
4th row71,69,182.00
5th row1,04,66,333.00

Common Values

ValueCountFrequency (%)
4,41,10,571.001
 
0.1%
1,26,01,747.001
 
0.1%
11,92,48,730.001
 
0.1%
4,78,43,891.001
 
0.1%
7,14,03,356.001
 
0.1%
79,50,217.001
 
0.1%
76,45,384.001
 
0.1%
6,91,59,100.001
 
0.1%
2,95,72,724.001
 
0.1%
3,04,47,187.001
 
0.1%
Other values (158)158
 
16.0%
(Missing)820
83.0%

Length

2023-06-23T14:14:35.096602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4,41,10,571.001
 
0.6%
96,85,432.001
 
0.6%
9,20,42,265.001
 
0.6%
9,37,76,985.001
 
0.6%
6,40,60,948.001
 
0.6%
1,64,50,546.001
 
0.6%
72,70,809.001
 
0.6%
2,73,55,808.001
 
0.6%
6,15,87,891.001
 
0.6%
7,39,61,482.001
 
0.6%
Other values (158)158
94.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Brazil_import
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct168
Distinct (%)100.0%
Missing820
Missing (%)83.0%
Memory size7.8 KiB
1,24,22,823.00
 
1
2,90,01,120.00
 
1
3,91,01,278.00
 
1
99,82,316.00
 
1
63,02,811.00
 
1
Other values (163)
163 

Length

Max length14
Median length14
Mean length13.28571429
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)100.0%

Sample

1st row26,49,293.00
2nd row27,87,667.00
3rd row46,92,701.00
4th row25,65,068.00
5th row46,71,325.00

Common Values

ValueCountFrequency (%)
1,24,22,823.001
 
0.1%
2,90,01,120.001
 
0.1%
3,91,01,278.001
 
0.1%
99,82,316.001
 
0.1%
63,02,811.001
 
0.1%
27,87,667.001
 
0.1%
48,99,089.001
 
0.1%
88,85,548.001
 
0.1%
99,93,670.001
 
0.1%
5,26,24,471.001
 
0.1%
Other values (158)158
 
16.0%
(Missing)820
83.0%

Length

2023-06-23T14:14:35.268468image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,24,22,823.001
 
0.6%
46,51,157.001
 
0.6%
99,16,342.001
 
0.6%
3,04,66,725.001
 
0.6%
4,44,68,278.001
 
0.6%
4,36,35,104.001
 
0.6%
1,47,84,129.001
 
0.6%
71,35,979.001
 
0.6%
4,10,33,475.001
 
0.6%
1,21,78,251.001
 
0.6%
Other values (158)158
94.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

France_import
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct240
Distinct (%)100.0%
Missing748
Missing (%)75.7%
Memory size7.8 KiB
6,42,21,842.00
 
1
7,09,19,799.00
 
1
6,97,56,851.00
 
1
10,40,58,924.00
 
1
4,30,70,443.00
 
1
Other values (235)
235 

Length

Max length15
Median length14
Mean length14.0375
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique240 ?
Unique (%)100.0%

Sample

1st row3,52,39,920.00
2nd row3,67,16,752.00
3rd row4,51,44,561.00
4th row3,22,40,349.00
5th row2,93,51,633.00

Common Values

ValueCountFrequency (%)
6,42,21,842.001
 
0.1%
7,09,19,799.001
 
0.1%
6,97,56,851.001
 
0.1%
10,40,58,924.001
 
0.1%
4,30,70,443.001
 
0.1%
2,79,31,017.001
 
0.1%
3,19,44,858.001
 
0.1%
7,08,15,398.001
 
0.1%
1,86,33,889.001
 
0.1%
6,20,77,415.001
 
0.1%
Other values (230)230
 
23.3%
(Missing)748
75.7%

Length

2023-06-23T14:14:35.440346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6,42,21,842.001
 
0.4%
7,09,19,799.001
 
0.4%
9,75,45,636.001
 
0.4%
7,38,54,538.001
 
0.4%
3,07,66,352.001
 
0.4%
3,96,57,146.001
 
0.4%
6,76,56,830.001
 
0.4%
6,97,83,270.001
 
0.4%
1,66,60,244.001
 
0.4%
7,45,35,569.001
 
0.4%
Other values (230)230
95.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Germeny_import
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct178
Distinct (%)100.0%
Missing810
Missing (%)82.0%
Memory size7.8 KiB
14,94,23,147.00
 
1
12,66,74,204.00
 
1
12,08,09,253.00
 
1
12,45,62,880.00
 
1
11,26,31,565.00
 
1
Other values (173)
173 

Length

Max length15
Median length15
Mean length14.79775281
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)100.0%

Sample

1st row12,27,44,998.00
2nd row14,92,83,636.00
3rd row14,42,63,050.00
4th row17,87,29,898.00
5th row14,99,58,877.00

Common Values

ValueCountFrequency (%)
14,94,23,147.001
 
0.1%
12,66,74,204.001
 
0.1%
12,08,09,253.001
 
0.1%
12,45,62,880.001
 
0.1%
11,26,31,565.001
 
0.1%
12,80,51,044.001
 
0.1%
9,15,35,329.001
 
0.1%
11,95,63,248.001
 
0.1%
10,59,03,120.001
 
0.1%
9,43,42,407.001
 
0.1%
Other values (168)168
 
17.0%
(Missing)810
82.0%

Length

2023-06-23T14:14:35.596567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14,94,23,147.001
 
0.6%
15,14,67,079.001
 
0.6%
11,17,29,748.001
 
0.6%
9,11,08,633.001
 
0.6%
10,44,06,806.001
 
0.6%
12,11,09,577.001
 
0.6%
13,86,68,637.001
 
0.6%
12,82,66,016.001
 
0.6%
9,82,45,960.001
 
0.6%
8,17,97,957.001
 
0.6%
Other values (168)168
94.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

United Kingdome_import
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct154
Distinct (%)100.0%
Missing834
Missing (%)84.4%
Memory size7.8 KiB
6,84,41,589.00
 
1
5,41,71,558.00
 
1
6,00,27,527.00
 
1
10,36,58,888.00
 
1
5,90,20,631.00
 
1
Other values (149)
149 

Length

Max length15
Median length14
Mean length14.01948052
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique154 ?
Unique (%)100.0%

Sample

1st row5,26,77,816.00
2nd row5,58,72,515.00
3rd row6,00,27,527.00
4th row7,11,04,465.00
5th row6,24,48,243.00

Common Values

ValueCountFrequency (%)
6,84,41,589.001
 
0.1%
5,41,71,558.001
 
0.1%
6,00,27,527.001
 
0.1%
10,36,58,888.001
 
0.1%
5,90,20,631.001
 
0.1%
5,43,38,957.001
 
0.1%
6,04,13,161.001
 
0.1%
5,49,57,367.001
 
0.1%
5,52,37,644.001
 
0.1%
5,58,72,515.001
 
0.1%
Other values (144)144
 
14.6%
(Missing)834
84.4%

Length

2023-06-23T14:14:35.768432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6,84,41,589.001
 
0.6%
8,40,37,080.001
 
0.6%
6,69,49,871.001
 
0.6%
4,81,69,995.001
 
0.6%
7,09,26,561.001
 
0.6%
5,54,82,301.001
 
0.6%
5,44,87,306.001
 
0.6%
8,63,65,913.001
 
0.6%
9,05,45,692.001
 
0.6%
8,79,48,739.001
 
0.6%
Other values (144)144
93.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

China_import
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct108
Distinct (%)100.0%
Missing880
Missing (%)89.1%
Memory size7.8 KiB
66,74,25,297.00
 
1
53,93,47,414.00
 
1
54,48,92,883.00
 
1
74,86,70,980.00
 
1
33,13,10,352.00
 
1
Other values (103)
103 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique108 ?
Unique (%)100.0%

Sample

1st row45,24,61,873.00
2nd row33,13,10,352.00
3rd row48,45,22,754.00
4th row38,03,60,106.00
5th row35,64,95,250.00

Common Values

ValueCountFrequency (%)
66,74,25,297.001
 
0.1%
53,93,47,414.001
 
0.1%
54,48,92,883.001
 
0.1%
74,86,70,980.001
 
0.1%
33,13,10,352.001
 
0.1%
57,93,20,983.001
 
0.1%
54,13,40,729.001
 
0.1%
52,92,98,039.001
 
0.1%
34,47,91,070.001
 
0.1%
46,98,43,345.001
 
0.1%
Other values (98)98
 
9.9%
(Missing)880
89.1%

Length

2023-06-23T14:14:35.909051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
66,74,25,297.001
 
0.9%
55,24,85,132.001
 
0.9%
49,25,83,672.001
 
0.9%
62,73,86,348.001
 
0.9%
59,23,58,515.001
 
0.9%
64,73,06,164.001
 
0.9%
47,43,65,708.001
 
0.9%
66,52,13,033.001
 
0.9%
43,35,83,338.001
 
0.9%
88,65,59,172.001
 
0.9%
Other values (98)98
90.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Japan_import
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct153
Distinct (%)100.0%
Missing835
Missing (%)84.5%
Memory size7.8 KiB
74,27,185.00
 
1
2,32,25,513.00
 
1
1,27,51,384.00
 
1
2,43,97,455.00
 
1
2,60,97,012.00
 
1
Other values (148)
148 

Length

Max length14
Median length14
Mean length13.80392157
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique153 ?
Unique (%)100.0%

Sample

1st row31,33,547.00
2nd row25,21,829.00
3rd row22,88,267.00
4th row32,81,961.00
5th row39,75,382.00

Common Values

ValueCountFrequency (%)
74,27,185.001
 
0.1%
2,32,25,513.001
 
0.1%
1,27,51,384.001
 
0.1%
2,43,97,455.001
 
0.1%
2,60,97,012.001
 
0.1%
1,47,07,676.001
 
0.1%
1,19,55,639.001
 
0.1%
1,55,66,395.001
 
0.1%
1,60,01,844.001
 
0.1%
1,72,51,474.001
 
0.1%
Other values (143)143
 
14.5%
(Missing)835
84.5%

Length

2023-06-23T14:14:36.080914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
74,27,185.001
 
0.7%
2,79,34,178.001
 
0.7%
93,72,165.001
 
0.7%
1,37,49,434.001
 
0.7%
2,69,40,858.001
 
0.7%
1,95,34,323.001
 
0.7%
1,55,07,089.001
 
0.7%
2,79,86,814.001
 
0.7%
2,20,79,921.001
 
0.7%
2,16,18,332.001
 
0.7%
Other values (143)143
93.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

South_korea_import
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing988
Missing (%)100.0%
Memory size7.8 KiB

Interactions

2023-06-23T14:14:06.374438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:12:51.670962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:12:55.733208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:12:59.426554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:03.305214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:06.936966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:10.653357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:14.515373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:18.197438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:22.051355image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:25.584808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:29.462444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:33.169695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:36.965867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:40.352993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:44.163594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:47.945911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:51.307526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:55.247778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:58.823719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:14:02.691728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:14:06.571683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:12:51.934609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:12:55.918849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:12:59.751638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:03.503170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:07.136487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:10.866170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:14.698720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:18.417610image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:22.235161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:25.802003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:29.648944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:33.367531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:37.131906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:40.541975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:44.356826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:48.125398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:51.524730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:55.435369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:59.005947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:14:02.855211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:14:06.763429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:12:52.128903image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:12:56.110401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:12:59.924039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:03.673839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:07.292026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:11.037014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:14.884719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:18.581759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:22.413877image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:25.968335image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:29.836392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:33.545910image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:37.302635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:40.718164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:44.542741image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:48.291453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:51.708028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:55.607245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:59.197832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:14:03.031878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:14:06.963392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:12:52.310578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:12:56.284084image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:00.155911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:03.836530image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:07.486759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:11.211941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:15.067482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:18.764642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:22.588476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:26.281008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:30.012802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:33.731772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:37.505306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:40.910151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:44.726106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2023-06-23T14:13:48.494744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-06-23T14:14:36.815238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-06-23T14:14:37.377701image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-06-23T14:14:37.908915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
2023-06-23T14:14:14.548283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-23T14:14:16.036671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-06-23T14:14:22.227986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DateDomestic Market (Contract) Blow Molding, LowSpot/Export Blow MoldingSpot, DomesticWTISPLCMCOILBRENTEUGASREGMIMPCHEXPCHPRUBBUSDMWPUFD4111PCU325211325211PCU32611332611301WPU0915021625PCU3252132521MHHNGSPWPU072205011PCU32611132611115PCU32611332611301.1PCU32611132611112WPU0915021622Producer Price Index by Industry: Plastics Material and Resins Manufacturing: Thermoplastic Resins and Plastics MaterialsAustralia _exportCanada_exportSaudi_exportUsa_exportIndia_exportRussia_exportSouth_Africa_exportTurkeyBrazilFrance_exportGermeny_exportUnited Kingdome_exportChina_exportAustralia _importCanada_importSaudi_importUsa_importIndia_importRussia_importSouth_Africa_importTurkey_importBrazil_importFrance_importGermeny_importUnited Kingdome_importChina_importJapan_importSouth_korea_import
001-01-200041.0NaNNaN27.1825.511.2896902.1863.129.207387135.0158.3106.3NaNNaN2.42NaNNaN106.3NaNNaN139.4NaNNaNNaNNaNNaNNaNNaN61,808.001,03,52,529.002,93,27,224.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN68,33,222.0026,49,293.003,52,39,920.00NaNNaNNaNNaNNaN
101-02-200041.0NaNNaN29.3527.781.3776584.4972.733.391099136.0159.5105.6NaNNaN2.66NaNNaN105.6NaNNaN141.7NaNNaNNaNNaNNaNNaNNaN4,50,675.0088,44,172.003,35,06,035.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN56,47,987.0027,87,667.003,67,16,752.00NaNNaNNaNNaNNaN
201-03-200045.0NaNNaN29.8927.491.5166424.11330.530.941913136.0163.2106.1NaNNaN2.79NaNNaN106.1NaNNaN146.3NaNNaNNaNNaNNaNNaNNaN6,71,273.001,40,91,209.003,57,64,935.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN67,21,125.0046,92,701.004,51,44,561.00NaNNaNNaNNaNNaN
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979NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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984NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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